Types Of Electric Vehicles Research Articles

Optimal Integration for DGs with EVs planning in Distribution Networks with Hybrid MC-GA Technique using Realistic Load Models

With the growing integration of Distributed Generations (DGs) and Electric Vehicles (EVs) into distribution grids, the need for efficient and intelligent management strategies has become paramount. The need for power consumption is increasing every day in accordance with the standard demand pattern. As a result, either creating a lot of electricity or minimizing losses is crucial. In order to minimize real and reactive power losses from the system's point of view, this paper presents the optimal performance index-based size and location determination of DGs, EVs and their coordination in distribution networks using realistic load models (RLMs) such as RLM-1, RLM-2, RLM-3, RLM-4, and RLM-5, respectively. Improvement of the system power factor (SPF) is defined in this analysis as the power system's performance both with and without different kinds of DGs and DGs with EVs for various RLMs. PHEVs type of EVs used in this paper. The hybrid Monte Carlo-Genetic Algorithm (MC-GA) is the basis of the simulation technique that is being suggested. Networks with IEEE-16 and 37 buses in their distribution systems have been used to test the suggested methods. Comprehensive simulations on a test distribution network are used to show that the suggested method is effective. The system's performance can be optimized in terms of grid stability, efficiency, and reliability, according to the results. By stressing the significance of employing a hybrid MC-GA technique to meet realistic load circumstances, this research advances optimal DG and EV solutions.

Modeling EV dynamic wireless charging loads and constructing risk constrained operating strategy for associated distribution systems

Dynamic wireless charging (DWC) is an emerging technology that enables the charging of electric vehicles (EVs) while they are in motion. However, previous load modeling methods have not thoroughly explored the detailed analysis of DWC load characteristics. Existing research only considers the single-node supply mode for dynamic wireless charging roads (DWCRs), and the assessment of operational risks arising from the uncertain DWC loads has not been addressed. This paper begins by conducting an equivalent circuit analysis of a typical EV DWC system with multiple segmented coils. We present a more accurate trapezoidal power model for a single EV. Subsequently, we model the aggregated EV DWC load, accounting for traffic flow and headway using Poisson and negative exponential distribution functions, respectively. In the operation process, we consider a multi-node supply mode for DWCRs. To address the inaccuracy of long-term predictions, we propose a rolling optimization model to coordinate DWC and renewables with heterogeneous uncertainties by introducing a risk metric to manage potential uncertain risks. The proposed optimization model is transformed into a mixed-integer second-order cone programming (MISOCP) problem after convex relaxation. Finally, we conduct case studies to validate the proposed methods.

The Influence of Incentives, Quality, and Product Availability on Attitude toward Purchase Decision of Battery Electric Vehicle at the Greater Jakarta

Transport and mobility have significant contributions to the emissions in urban cities in Indonesia. The Indonesian government has supported the adoption of electric vehicles (EVs) as part of the commitment to zero emissions by 2060. According to The Association of Indonesia Automotive Industries (GAIKINDO), in 2022 the electric vehicle contributes 1.49% of the total domestic passenger car sales in Indonesia. This EV population was too small compared to what the government targeted for the adoption of EVs. This study aims to analyze the influence of government incentives, product quality, and product availability on consumer’s attitudes toward purchase decisions of battery electric vehicles (BEV). A sample of 149 respondents, who have a driving license, live in the greater area of Jakarta were collected through a questionnaire-based primary data. Through a rigorous procedure of structural equation modeling (Smart-PLS), it is found that variables of government incentives and product quality have no significant effect on consumer purchase decision for BEVs. Product availability and attitude variable has positive and significant effect on consumer purchase decision for BEVs. Moreover, the product availability variable is imperative since it is found to be the most significant variable construct. Thus, as the managerial implication, BEV dealer and manufacturer could consider the availability of various choices of electric cars with the price and driving range capabilities. Future research is recommended to cover all types of EVs, collect wider respondents on different areas and also explore different variables that influence EV customer purchase decision.

A Novel Neuro-Probabilistic Framework for Energy Demand Forecasting in Electric Vehicle Integration

This paper presents a novel grid-to-vehicle modeling framework that leverages probabilistic methods and neural networks to accurately forecast electric vehicle (EV) charging demand and overall energy consumption. The proposed methodology, tailored to the specific context of Medellin, Colombia, provides valuable insights for optimizing charging infrastructure and grid operations. Based on collected local data, mathematical models are developed and coded to accurately reflect the characteristics of EV charging. Through a rigorous analysis of criteria, indices, and mathematical relationships, the most suitable model for the city is selected. By combining probabilistic modeling with neural networks, this study offers a comprehensive approach to predicting future energy demand as EV penetration increases. The EV charging model effectively captures the charging behavior of various EV types, while the neural network accurately forecasts energy demand. The findings can inform decision-making regarding charging infrastructure planning, investment strategies, and policy development to support the sustainable integration of electric vehicles into the power grid.

Leveraging online reviews and expert opinions for electric vehicle type prioritization

The rapid expansion of the electric vehicle (EV) market in recent years has presented manufacturers with the challenge of strategically prioritizing the types of EVs in which to invest under uncertain conditions. This study proposes an enhanced multi-attribute decision-making (MADM) framework to address this issue by leveraging online reviews and expert opinions. The proposed framework combines a novel linguistic representation, called calibrated basic uncertain linguistic information (CBULI), to capture uncertainty, a value function from cumulative prospect theory (CPT) with double reference points to model the manufacturers’ psychological preferences, and the Preference Ranking Organization Method for Enrichment of Evaluations II (PROMETHEE II) for prioritizing EVs for investment. It extracts demand attributes from online reviews, applies CBULI to represent uncertain evaluations, and incorporates CPT to capture risk preferences. A case study of an EV manufacturing enterprise in Sichuan, China, was conducted to validate our framework, and the results demonstrated its effectiveness and practicability in identifying the most promising types of EVs for investment. The results of sensitivity and comparative analyses further confirmed the robustness and superiority of the model in comparison with prevalent methods. The work here contributes to methodological advancements in research on the choice of types of EVs in which to invest, and provides valuable insights for EV enterprises to make informed investment-related decisions that are aligned with user demands and enterprise development. The proposed MADM framework supports the strategic development of the EV industry by enabling manufacturers to prioritize investment in the appropriate types of EVs under uncertainty.

Analyzing Energy Efficiency and Battery Supervision in Electric Bus Integration for Improved Urban Transport Sustainability

Addressing the critical challenge of reducing local emissions through the electrification of urban public transport, this research specifically focuses on integrating electric buses. The primary objectives are to evaluate energy efficiency and ensure battery cell supervision. Introducing electric buses plays a significant role in reducing emissions, contributing to more sustainable urban transport systems. However, this transition introduces a set of new challenges, including the complexities of electric charging logistics, the establishment of new consumption standards, and the intricate relationships between distance traveled, ambient temperature, passenger load, and battery health. Methodologically, this study collects and examines factors impacting energy consumption, including external temperatures, bus conditions, road conditions, and driver behavior. By analyzing these variables, a baseline for actual consumption can be established, allowing for the calculation of an energy balance to identify energy inefficiencies. This enables the optimization of route planning, the strategic selection of stops, and the efficient scheduling of charging times, along with ensuring the proper scaling of the bus battery system. This study found that energy consumption peaked at 116.73 kWh/100 km in the lowest temperature range of −5 °C to 0 °C. Consumption decreased significantly with rising temperatures, dropping by 25 kWh between 5 °C and 10 °C and by an additional 10 kWh between 10 °C and 15 °C. Beyond 20 °C, variations were more influenced by route and driving style than by temperature. Route and driver variability significantly influenced energy consumption, with up to threefold differences across routes due to factors such as road type and traffic volume. Additionally, there was a 31.85% difference between the most and least efficient drivers, highlighting the critical impact of driving style. Furthermore, this study explores the assessment of battery systems through cell-level diagnostics to detect potential faults. Considering that buses are equipped with significantly more batteries than typical electric vehicles, detecting and localizing faults at the cell level is crucial to avoid the substantial costs and environmental impact associated with replacing large battery systems. Utilizing the results of this research and the applied examination methods, it is possible to enhance energy efficiency and extend battery life, thereby contributing to the development of more sustainable and cost-effective urban transport solutions.

A Universal Electric Vehicle Outlet and Portable Cable for North America

For electric vehicle (EV) charging in North America, three AC connectors are standardized, resulting in a proliferation of charging stations which can only charge one of the three types of EV. We propose a “Universal EV Outlet” that works with an EV “carry along” charging cable—one end of the cable has a connector specific to that user’s EV, the other a plug for the Universal EV Outlet. This proposal does not interfere with, nor require change to, any existing charging stations. It does not require any new types of inlets on EVs. The components are already standardized. Eight use cases are examined to illustrate the advantages, and some limitations, of the Universal EV Outlet. The use cases illustrate how this solution: resolves the problem of multiple AC charging connectors, makes today’s “EV Ready” building codes more adaptable, lowers capital and maintenance costs, creates a solution to curbside and urban charging, increases energy efficiency, enables higher power three-phase AC charging for heavy vehicles, and facilitates use of EVs for building backup power and for vehicle-to-grid. Finally, we propose a standards-based active cable used with the Universal EV Outlet, which would allow fast and secure EV identification for curbside or other shared charging locations, usable today without modifications to current EVs.

Electric vehicle (EV) type choice model: Latent class modelling (LCM) approach

This study develops a flexible Latent Class Model (LCM) to investigate the electric vehicle (EV) type choice decisions of Halifax residents. It utilizes cross-sectional data from the 2022 Halifax Travel Activity (HaliTRAC) survey, which includes questions related to EV adoption. This study also analyzes eight attitude and lifestyle preference related statements using principal component analysis (PCA) technique and finally extracts three components labeled as “EV enthusiasts”, “Sustainable travellers”, and “Remote work arrangement admirers”. This paper explores the heterogeneity between two classes for different alternative vehicle type choice, such as, battery electric vehicle (BEV), plug-in hybrid electric vehicle (PHEV), hybrid electric vehicle (HEV), and regular internal combustion engine (ICE) vehicle. Based on class membership attributes, class-1 can be identified as those who live in suburban areas, have a large family with high vehicle ownership, and are interested in travelling with their family members, especially with their children and vice-versa for class-2. Results suggest that variables across two classes portray heterogeneity, such as, full-time worker portray positive correlation for class-1 and negative to class-2; high annual household income group (more than $200,000) exhibit high propensity to choose BEV in class-2 and vice-versa for class-1. Sustainable travelers emphasize the adverse connection towards regular vehicles, while EV enthusiasts demonstrate a favorable association with embracing any type of EV (BEV, PHEV, HEV). Furthermore, the findings from this analysis provide guidance for policy measures such as offering purchase incentives, expanding charging infrastructure, and implementing tax rebates to promote the uptake of EV among the residents of Halifax.

Electric vehicle fast charging station energy management system for radial distribution network with a photo-voltaic distributed generator (PV-DG)

As the demand for electric vehicles (EVs) increases in all countries, automobile companies are launching different types of EVs. Installation of EVCS in the present power system network without modification can cause an extra burden on the present network. However, this extra burden can be reduced if EVCS gets a supply of renewable energy resources. This paper proposes energy management schemes by installing a photo-voltaic distributed generator (PV-DG) and energy storage system (ESS) at an EVCS. An IEEE-33 bus system for the Saudi Arabia United Kingdom rural location has been selected to introduce real-time energy management schemes. In this radial distribution network (RDN), three EVCS of different capacities are installed at different locations to maintain the stability of the network. The power flow program is used in the MATLAB environment to check the stability of the network. Accurate solar data, traffic patterns, and EV charging patterns in Saudi Arabia are collected and used for energy management at EVCS. Three cases are used for comparison: EVCS annual charges, EVCS annual benefits, and grid annual fuel charges. All proposed cases are also simulated in MATLAB using MATPOWER6 to check the impact on voltage profile and power line flows.

Electrifying the road: Navigating the transition to electric vehicles in Connecticut through hybrid insights and fleet evolution

The adoption of light duty electric vehicles (EVs) in the past 5 years has accelerated dramatically. As EVs continue penetrating the U.S. market, it is critical to understand what a sustainable transition within the transportation sector will look like in relation to mitigating impacts of climate change. This work aims to fill two existing gaps in the current literature: 1) investigate the positive and negative potential influences of the emerging EV transition within the U.S. market and 2) investigate the types of vehicles EVs are replacing. We fulfill these objectives by utilizing a unique attribute dataset of all vehicles in Connecticut (USA) on the road between 2013 and 2020. Our findings suggest that the penetration rate of traditional hybrid vehicles (e.g., Toyota Prius) is an important predictor of EV adoption. Furthermore, our results show that in Connecticut most of that transition is occurring among hatchbacks (e.g., Chevrolet Bolt), a body style where internal combustion engine (ICE) versions have relatively high fuel efficiencies compared with other major vehicle types (e.g., pickup trucks or SUVs). Our results offer important implications for policymakers in terms of maximizing the deployment of EVs and EV infrastructure and the path in which the automotive sector is progressing towards decarbonization.

The home health care routing with heterogeneous electric vehicles and synchronization

AbstractThis paper studies the problem of heterogeneous electric vehicles, fast chargers, and synchronized jobs that have time windows in home healthcare routing and scheduling. We consider a problem that aims to establish daily routes and schedules for healthcare nurses to provide a variety of services to patients located in a scattered area. Each nurse should be assigned to an electric vehicle (EV) from a heterogeneous fleet of EVs to perform the assigned jobs within working hours. We consider three different types of EVs in terms of battery capacity and energy consumption. We aim to minimize the total cost of energy consumption, fixed nurse cost, and costs arising from the patients that cannot be served within the working day. We model the problem as a mixed integer programming formulation. We develop a hybrid metaheuristic based on a greedy random adaptive search procedure heuristic, to generate good quality initial solutions quickly, and an adaptive variable neighborhood search algorithm to generate high quality solutions in reasonable time. The hybrid metaheuristic employs a set of new advanced efficient procedures designed to handle the complex structure of the problem. Through extensive computational experiments, the performance of the mathematical model and the hybrid metaheuristic are evaluated. We conduct analyses on the robustness of the metaheuristic and the performance contribution of employing adaptive probabilities. We analyze the impact of problem parameters such as competency requirements, job duration, and synchronized jobs.

Marketing Strategy and Preference Analysis of Electric Cars in a Developing Country: A Perspective from the Philippines

The wide-scale integration of electric vehicles (EVs) in developed countries represents a significant technological innovation and a step toward reducing carbon emissions from transportation. Conversely, in developing nations like the Philippines, the adoption and availability of EVs have not been as rapid or widespread compared to other countries. In identifying this gap, this study delved into the preferences and factors influencing Filipino consumers’ willingness to purchase EVs. The study gathered 311 valid responses utilizing conjoint analysis with an orthogonal approach to assess the attributes influencing customers’ purchase decisions. Conjoint analysis tools such as IBM SPSS v25 statistics were utilized to infer consumer preference. The results determined that cost is the primary concern for consumers by a considerable margin; followed by battery type and charging method; along with the type of EV, driving range, and charging speed; and most minor concern is regenerative brakes. Therefore, there is an apparent sensitivity to price and technology. This study is the first to apply conjoint analysis to the Philippine market, delivering in-depth consumer preference insights that can help manufacturers and policymakers customize their approach to making EVs more attractive and more viable in less developed markets. The results suggest that a targeted effort to overcome cost barriers and improve technological literacy among prospective buyers should be productive for speeding up EV adoption in the Philippines. The results could be extended in future research to a broader assessment of socioeconomic and environmental benefits, laying out a broader plan for promoting sustainable solutions in transportation.

Conversion of metro railway into smart charging zero-emission transportation hub for supporting electric train, shuttle bus, cars and public EV charging station

The unprecedented electrification of the transportation sector and rapid renewable energy penetration create multiple issues on available grid networks. Large-scale integration of various electric vehicle (EV) types into a distributed renewable energy system has not been studied before, and their systematic integration method and quantitative impacts are still unclear in academia. Therefore, this project converted an existing net-zero metro railway network into a smart charging EV hub by transforming the stations to smartly charge all types of EV intersecting at a station, such as public EV charging stations, electric shuttle buses and metro-owned EV cars. Accordingly, the methodology begins with the multiobjective analyses of integrating a public EV charging station into a swappable battery-powered metro station. Secondly, an electric shuttle bus is integrated into the stations without and with bidirectional energy sharing and evaluated for the impact of driving patterns, parking time, and schedule coordination. Thirdly, the metro-owned EV cars are incorporated into the stations and analysed for the number of journeys without and with bidirectional energy sharing. Finally, the importance of control priority while designing the smart charging EV hub is examined. Overall, converting a metro railway into a smart charging EV hub significantly increases the renewable energy consumption from 0.58 to 0.76, improves the net present value from 71.35 × 106 HKD to –35.02 × 106 HKD, and reduces the annual maximum grid import from 1099 kW to 777 kW. The success of vehicle-to-building energy sharing mainly depends on the driving demand ratio to the battery size.

Pro-environment consumer behaviour and electric vehicle adoptions: a comparative regional meta-analysis

ABSTRACT Numerous studies have explored the relationship between the environment and the adoption of electric vehicles (EVs); however, a consensus has not been reached among empirical findings. This study aims to conduct a comprehensive meta-regression analysis by synthesizing and scrutinizing data from existing literature to identify potential sources of heterogeneity among effect sizes in individual studies. To achieve this goal, we collected 429 estimates from 65 previously published studies spanning the period from 1998 to 2021. We investigate the connection between pro-environmental consumer behaviour and EV adoption at the aggregate level and compare this relationship across three distinct regions: North America, Western Europe, and emerging markets. Our research employs advanced empirical techniques, including Bayesian model averaging, frequentist model averaging, and weighted average least squares. The outcome of our analysis reveals a positive, albeit relatively weak synthesized effect of environmental factors on EV adoption. Notably, our meta-analysis findings indicate that the impact of environmental concerns on EV adoption varies significantly depending on the specific type of EV being considered. Consumers prefer versatile hybrid and plug-in hybrid electric vehicles due to fuel flexibility, especially in areas where battery electric vehicle charging infrastructure is scarce.

Stochastic Modelling and Integration of Electric Vehicles in the Distribution System Using the Jaya Algorithm

Electric vehicles (EVs) have enormous promise for the development of future transportation systems. The widespread use of EVs could negatively impact how power systems operate, particularly at the distribution level. Therefore, smart charging techniques are essential to increasing EV adoption in general. The connection between the electricity grid and the transportation network is made at electric vehicle charging stations (EVCS), and both networks will be simultaneously impacted by the operational behaviour of EVs. Therefore, EVCS must be placed in a distribution network in the best possible way. In this paper, an efficient and smart charging approach is formulated to schedule the charging of electric vehicles so that adverse effects like an increase in peak demand for the system and the cost of charging electric vehicles are minimized. The EV load model is formulated by considering factors such as the state of charge, trip distance travelled, and the user’s charging behaviour. The proposed electric charging schedule reduces the peak load and optimizes the cost of charging. Different types of EVs are considered based on their usage patterns for making realistic problem formulations. The smart charging technique presented in this work reduces the peak demand by 30% and the cost of charging by 50%. In addition, EVCS placement is implemented alongside distributed generation on IEEE 33 and 69 bus systems to reduce power losses and improve the voltage profile by using a metaheuristic algorithm known as Jaya Algorithm. The effectiveness of the proposed algorithm is established by comparing the results with published work.

Charging/Refueling Navigation Strategies for Plug-in Hybrid Hydrogen and Electric Vehicles With Irrationalities and Energy Substitution

Electric vehicles (EVs) are believed to be effective in reducing the use of fossil fuels. However, the increasing penetration of EVs can lead to challenges in managing EV charging/refueling. Typically, when EVs simultaneously gather at the stations, long queueing lengths and waiting times might occur, which might result in EV disutility. Thus, in this paper, novel decision-making strategies considering the irrational behaviors of EVs are formulated to ensure the utility of EVs. Three types of EVs are investigated, i.e., plug-in electric vehicles (PEVs), fuel cell electric vehicles (FCEVs), and plug-in hybrid hydrogen and electric vehicles (PH2EVs). First, three typical types of irrationalities are modeled based on prospect theory and irrational herding theory, including risk attitude irrationality, probability distortion, and irrational herding behaviors. Second, decision-making strategies, including station selection, navigation, and energy purchasing, are modeled based on EV irrationalities. Additionally, when making the station selection decision, the selection ranking index is proposed to balance energy price and waiting time. Third, the energy substitution of PH2EV is investigated to determine the optimal energy-purchasing mixture of electricity and hydrogen. Simulation results show that the average waiting time and charging/refueling time of EV users of the proposed model are reduced compared with the other two cases where EV users are assumed to be rational. In addition, the overall utility of EV users is enhanced.

An exact algorithm for maximum electric vehicle flow coverage problem with heterogeneous chargers, nonlinear charging time and route deviations

Typical electric vehicles (EVs) should be recharged multiple times to reach a long distance due to the limited travel ranges. The EV flow covering problem determines the locations of charging stations to maximize the “covered” OD flows, respecting a given budget for the new charging stations. This paper proposes an exact algorithm for solving the problem by considering the heterogeneous EV chargers, nonlinear charging time, and route deviations from the shortest distance paths. We develop an exact Benders decomposition-based algorithm, in which the Benders subproblem utilizes a nested cut-generation via a customized labeling algorithm. Two methods for solving the Benders subproblem are developed, which are combined to accelerate the Benders cut generation. In addition, we present a heuristic algorithm based on the proposed exact algorithm for the larger-sized problems. The computational results show that the proposed algorithm could solve real-life problems. Especially, the heuristic algorithm could solve problems up to 339 nodes and 500 km of the EV’s travel range. The extensive computational study shows that addressing the multiple charger types, the charging time, and the deviation routes can be crucial to provide realistic charging infrastructure for EVs.

Examination of the Factors Influencing the Electric Vehicle Accident Size in Norway (2020–2021)

With the great increase of electric vehicles (EVs) in the past decade, EV-involved traffic accidents have also been increasing quickly in many countries, bringing many new traffic safety challenges. Norway has the largest EV penetration rate in the world. Using the EV accident data from Norway in 2020 and 2021, this study aims to investigate the features of EV safety comprehensively. Firstly, a descriptive analysis is conducted. It has been found that rear-end collisions are the major collision type of EVs, and EVs are very likely to collide with pedestrians/cyclists. In addition, in terms of roadway type, EV accidents mainly occur on medium- and low-speed roads; in terms of environment, they mainly occur in good visibility conditions and dry road surface conditions. Then, a regression analysis is conducted to identify the key factors affecting the accident size, which is the number of traffic units involved in an accident and taken as the accident severity surrogate here. Since EV accidents are divided into four categories in order of accident size, the ordered logit model is adopted. It divides a multi-categorical dependent variable into multiple binary data points in order and calculates the probability of the dependent variable falling into each category with the logit model, respectively. The estimation results indicate that time of day, speed limit, and presence of medians have statistically significant impacts on the EV accident size. Finally, some countermeasures to prevent EV accidents are proposed based on the research results.

Ultra-Short-Term Load Forecast of Charging Stations Considering the Travelling and Charging Behaviours of Electric Vehicles

The traditional electric vehicle (EV) load forecasting methods are mostly used to predict large areas such as cities, which makes it difficult to serve practical applications. The existing charging station load forecasting methods ignore the traveling and charging behaviors of EVs. To this end, a charging station ultra-short-term load prediction method considering the traveling and charging behaviors of EVs is proposed. Firstly, EV types are classified according to charging behaviors. On this basis, a rolling forecast model of the number of arrivals per unit time of each vehicle type is established, and then the impact of charging randomness on load forecast is reduced by repeated Monte Carlo sampling. The total load of charging stations during the prediction period can be obtained by adding up the load of individual vehicles. The results show that the method enables accurate prediction of ultra-short-term load at charging stations, which has higher accuracy and reference value than the traditional load forecasting methods.

Optimization of distribution network operating parameters in grid tied microgrid with electric vehicle charging station placement and sizing in the presence of uncertainties

ABSTRACT The power generated by renewable sources is prone to uncertainties which encourages to perform effective modelling to ensure the reliable operation of the grid-connected microgrid (MG) system. This work focuses on the uncertainty modelling of primary sources of solar power, wind power and electric vehicle (EV) load considering three factors battery capacity, state of charge (SoC) and type of EVs charged by employing Monte-Carlo Simulation (MCS) to minimize the distribution system's voltage stability, reliability and power loss (VRP) index. The objective function is tested on the modified IEEE-33 bus distribution system under three diverse scenarios with optimum sizing and placement of RESs and EVCS. To obtain optimal solutions for the proposed problem in a reasonable computation time, modified version of teaching and learning-based optimization (TLBO) and the JAYA algorithm are applied as the rate of convergence is superior to other existing methods in the literature and does not require any precise control parameters. For all the scenarios, it can be seen that the modified JAYA algorithm outperforms TLBO and other existing approaches. The findings of the results reveal the efficacy of uncertainty modelling in a proposed grid-connected DC MG to curtail the VRP index.